Individuals have the right to access their personal data held by private companies. This operation can be started by different types of data subjects. A 2020 poll conducted among UK managers showed that ** percent of the requests came from employees or ex-employees. Another ** percent of Data Subject Access Requests (DSAR) were submitted by customers.

Dataset

This dataset was created by soumik saha

Contents

American Business Formations in the US

A Look at Business Applications by State and Category

By Andy Kriebel [source]

About this dataset

This dataset contains information on business applications filed in the United States by state and category. The data includes the number of business applications from corporations, as well as the month and state in which they were filed. This data can be used to track trends in business formation across the country and to compare the relative activity of different states

How to use the dataset

How to use this dataset

This dataset can be used to analyze the number of business applications filed in the United States by state and category. The data can be used to examine trends in business formation and to compare the rates of formation across states

Research Ideas

• Tracking changes in business applications by state and category over time
• Predicting future business formation trends based on historical data
• Identifying states and categories with the highest or lowest rates of business formation

Acknowledgements

Dataset: Business Formation Statistics

The data for this dataset was collected by the US Census Bureau

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Business Formation Statistics.csv | Column name | Description | |:--------------------------------------------|:----------------------------------------------------------------------------------| | month | The month the business application was filed. (Date) | | category | The category of business the application is for. (String) | | state abbr | The two letter abbreviation for the state the business is located in. (String) | | Business Applications from Corporations | The number of business applications filed by corporations in the state. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

Wholesale Trade: Subject Series - Misc Subjects: Detailed Type of Operation for the U.S.: 2012.

Vehicle Data Subject Access Requests Market Outlook

According to our latest research, the Global Vehicle Data Subject Access Requests market size was valued at $1.25 billion in 2024 and is projected to reach $5.89 billion by 2033, expanding at a robust CAGR of 18.7% during the forecast period of 2025–2033. The primary factor fueling this remarkable growth is the increasing emphasis on data privacy and regulatory compliance in the automotive sector, particularly as vehicles become more connected and generate vast amounts of personal and operational data. The proliferation of connected vehicles and the implementation of stringent data protection laws such as GDPR and CCPA are compelling automotive stakeholders to adopt advanced solutions for managing and responding to data subject access requests (DSARs), ensuring transparency and user rights in vehicle data handling.

Regional Outlook

North America currently commands the largest share of the global Vehicle Data Subject Access Requests market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature automotive industry, widespread deployment of connected vehicles, and proactive regulatory frameworks surrounding data privacy. The United States, in particular, has witnessed a surge in DSAR-related services following the enactment of the California Consumer Privacy Act (CCPA) and similar state-level regulations, compelling OEMs, fleet operators, and insurers to invest in robust data management and reporting systems. Furthermore, the presence of leading technology providers and a high rate of cloud adoption have accelerated the integration of advanced DSAR solutions across automotive enterprises, reinforcing North America’s leadership in this space.

The Asia Pacific region is poised to be the fastest-growing market for Vehicle Data Subject Access Requests, projected to register a staggering CAGR of 22.4% between 2025 and 2033. This accelerated growth is driven by rapid digital transformation in the automotive sector, burgeoning vehicle sales, and increasing awareness of data privacy rights among consumers. Countries like China, Japan, and South Korea are investing heavily in connected vehicle infrastructure and smart mobility solutions, which in turn necessitate robust data governance and compliance mechanisms. Additionally, government initiatives to harmonize data protection standards and the rising adoption of electric and autonomous vehicles are further propelling the demand for DSAR solutions across the region.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Vehicle Data Subject Access Requests solutions, albeit at a slower pace due to infrastructural and regulatory challenges. While these regions represent a smaller share of the global market—collectively accounting for less than 15% in 2024—their potential for future adoption is significant, especially as local governments begin to implement data privacy laws and automotive digitalization initiatives. However, limited awareness, fragmented data ecosystems, and a lack of standardized compliance frameworks currently pose hurdles to widespread DSAR adoption. Nevertheless, as international automotive brands expand their footprint and regulatory harmonization improves, these markets are expected to contribute meaningfully to global growth over the next decade.

Report Scope

From the beginning, since the first printed newspaper, every news that makes into a page has had a specific section allotted to it. Although pretty much everything changed in newspapers from the ink to the type of paper used, this proper categorization of news was carried over by generations and even to the digital versions of the newspaper. Newspaper articles are not limited to a few topics or subjects, it covers a wide range of interests from politics to sports to movies and so on. For long, this process of sectioning was done manually by people but now technology can do it without much effort. In this hackathon, Data Science and Machine Learning enthusiasts like you will use Natural Language Processing to predict which genre or category a piece of news will fall in to from the story. Size of training set: 7,628 records Size of test set: 2,748 records FEATURES: STORY: A part of the main content of the article to be published as a piece of news. SECTION: The genre/category the STORY falls in. There are four distinct sections where each story may fall in to. The Sections are labelled as follows : Politics: 0 Technology: 1 Entertainment: 2 Business: 3

This dataset contains 150,000 retail interaction records representing customer journeys in both e-commerce and in-store environments. It captures detailed behavioral, demographic, and product-related information to support research in product sales history, customer demographics, purchase patterns, personalized shopping experiences, customer behavior analysis, and predictive modeling.

Each row corresponds to a unique customer–product interaction, including session details, browsing or purchasing behavior, and applied discounts. The purchase column serves as the binary target variable (1 = purchased, 0 = not purchased), making the dataset suitable for various classification and recommendation tasks.

Key Features

Size: 150,000 rows × 19 columns

Target Column: purchase (binary: 1 = purchased, 0 = not purchased)

Data Types:

Categorical: User ID, product ID, interaction type, device type, product category, brand, location, gender

Numerical: Price, discount, age, loyalty score, previous purchase count, average purchase value

Temporal: Timestamp (to study trends and patterns)

Text: Search keywords

Behavioral Data: Interaction type (view, click, add to cart, purchase), purchase history statistics

Product Metadata: Category, brand, price, discount percentage

User Demographics: Age, gender, loyalty score

Applications:

Retail personalization

Purchase prediction

Customer segmentation

Behavioral pattern analysis

Information: Subject Series - Misc Subjects: Receipts by type of Dissemination Media for the U.S.: 2012.

Finance and Insurance: Subject Series - Misc Subjects: Type of Loan Services Income for the U.S.: 2012.

The dataset contains the following files.* y_test.pkl: Test data set labels.* y_pred_attention_20.pkl: predicted labels predicted by the attention layer (biGru + FastText + attention) after 30 iterations . * 2020-kandimalla-citeseerx-subject-areas: classification results of 1 million citeseerx papers.People can validate the accuracy and micro-F1 by using classification_report, confusion_matrix from sklearn framework.

Categories for Subject Matter - ARCHIVED

For the new LA City Events dataset (refreshed daily), see https://data.lacity.org/A-Prosperous-City/LA-City-Events/rx9t-fp7k

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Murlo" data publication.

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

Dataset

This dataset was created by hamza737

Released under CC0: Public Domain

Contents

Decoding functional category of source code from the brain (fMRI on Java program comprehension)

Original paper

TODO: write arXiv(?) URL of the paper here.

Overview

In this study, fMRI data was recorded while subjects were categorizing source code snippets into one of four functional categories. The experiment consisted of six separate runs (36 trials plus one dummy trial for each run) and a total of 72 Java code snippets were each presented three times. In each trial, a Java code snippet was displayed for ten seconds after a fixation-cross presentation for two seconds. Then, subjects classified the given code snippet into one of four category classes within four seconds by pressing a button. We recruited top- and middle-rated programmers as well as novice controls to cover a wide range of programming expertise using programmers' rate in the competitive programming contest 'AtCoder'. fMRI data in each subject were used to train and evaluate models (decoders) to predict functional category or subcategory of seen Java code snippets. Searchlight-based decoding accuracies were assessed to identify the brain regions that contribute expert programmers' outstanding performances on program comprehension.

Source code for preprocessing and analyses is available at GitHub (TODO: write repository URL here).

Dataset

MRI files

This dataset contains fMRI data from twenty-nine subjects ('sub-01', 'sub-02', ..., 'sub-29'). Each subject data contains anatomical and functional MRI data. Functional scans were collected over six scanning runs.

The functional EPI scans covered the entire brain (TR, 2000 ms; TE, 30 ms; flip angle, 75°; voxel size, 2 × 2 × 2.01 mm; FOV, 192 × 192 mm; slice gap, 0 mm). The dataset also includes a T1-weighted anatomical reference image for each subject (TR, 2530 ms; TE, 3.26 ms; flip angle, 9°; voxel size, 1.0 × 1.0 × 1.0 mm; FOV, 256 × 256 mm). The T1-weighted images were scanned only once for each subject. The T1-weighted images were defaced using mri_deface (https://surfer.nmr.mgh.harvard.edu/fswiki/mri_deface). All DICOM files are converted to Nifti by dcm2niix (version v1.0.20190902).

Note: We used MRI data from thirty subjects in the original paper. Twenty-nine subjects approved to open their MRI data to the public but one subject declined. Thus, we published the MRI data only from subjects who approved to make it open.

Subject information

The subject information file ('participants.tsv') denote the background information of each subject (age, sex, handedness, etc.). You can find what each column of the subject information files represents in './participants.json'.

Task event files

Task event files (‘sub-*_func_task-ProgramCategorization_run-*_events.tsv’) denote recorded event (stimuli code, subject responses, etc.) during fMRI runs. You can find what each column of the task event files represents in './task-ProgramCategorization_events.json'.

Java code snippets as the experimental stimuli

Java code snippets used in the study were stored in the stimuli directory ('./stimuli'). They were collected from an open codeset provided by AIZU ONLINE JUDGE (http://judge.u-aizu.ac.jp/onlinejudge/) and preprocessed by the authors to normalize indentation styles and names of user-defined functions. The 'stim_file' column in the task event files indicate one of the Java code snippets in the stimuli directory to specify which code snippet was used in each trial of the experiment.

Contact

• Email: takatomi-k@is.naist.jp

Environmental Pollution Incident data filtered for Categories 1 and 2. Details of environmental incidents within the remit of the Environment Agency are held on the National Incident Recording System (NIRS2). This dataset only includes substantiated completed and closed Environment Management incidents (predominantly pollution), where the environment impact level is either category 1 (major) or category 2 (significant) to at least 1 media (i.e. water, land or air). It is updated quarterly and provides a snapshot of data held in NIRS2. There is an inherent lag time in investigating and recording the necessary incident details to complete a record and recent incidents may not appear. The data may also be subject to change due to final QA and as further information becomes available. INFORMATION WARNING: Where these data indicate an incident occurred on a particular site or property no inference should be drawn that the site or property owner necessarily was responsible. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Foreign born excludes people born outside the United States to a parent who is a U.S. citizen..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..2019 ACS data products include updates to several categories of the existing means of transportation question. For more information, see: Change to Means of Transportation..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates o...